With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.

Multimedia Big Data Analytics: A Survey

In this paper, we present the software architecture of a complete mobile tourist guide for cultural heritage sites located in the old town of Chania, Crete, Greece. This includes gamified components that motivate the user to traverse the suggested interest points, as well as technically challenging outdoors augmented reality (AR) visualization features. The main focus of the AR feature is to superimpose 3D models of historical buildings in their past state onto the real world, while users walk around the Venetian part of Chania’s city, exploring historical information in the form of text and images. We examined and tested registration and tracking mechanisms based on commercial AR frameworks in the challenging outdoor, sunny environment of a Mediterranean town, addressing relevant technical challenges. Upon visiting one of three significant monuments, a 3D model displaying the monument in its past state is visualized onto the mobile phone’s screen at the exact location of the real-world monument, while the user is exploring the area. A location-based experience was designed and integrated into the application, enveloping the 3D model with real-world information at the same time. The users are urged to explore interest areas and unlock historical information, while earning points following a gamified experience. By combining AR technologies with location-aware and gamified elements, we aim to promote the technologically enhanced public appreciation of cultural heritage sites and showcase the cultural depth of the city of Chania.

https://www.mdpi.com/2220-9964/7/12/463/pdf

An Architecture for Mobile Outdoors Augmented Reality for Cultural Heritage

During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data ...

https://journals.sagepub.com/doi/pdf/10.1177/1847979019890771

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Coastal areas are quite fragile landscapes as they are among the most vulnerable to climate change and natural hazards. Coastline mapping and change detection are essential for safe navigation, resource management, environmental protection, and sustainable coastal development and planning. In this paper, we proposed a new methodology for the automatic extraction of coastline, using aerial images. This method is based on edge detection and active contours (snake method). Initially the noise of the image is reduced which is followed by an image segmentation. The output images are further processed to remove all small spatial objects and to concentrate on the spatial objects of interests. Then, the morphological operators are applied. We used aerial images taken from an aircraft and high-resolution satellite images from a coastal area in Crete, Greece, and we compared the results with geodetic measurements, to validate the methodology.

https://www.mdpi.com/2076-3263/8/11/407/pdf

Automatic Coastline Extraction Using Edge Detection and Optimization Procedures

. Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving fronts is time consuming, which limits the availability of these data across a wide spatial and temporal range. Automated methods face challenges that include the handling of clouds, illumination differences, sea ice melange, and Landsat 7 scan line corrector errors. To address these needs, we develop the Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers, using neural networks. The results are often indistinguishable from manually curated fronts, deviating by on average 86.76  ±  1.43 m from the measured front. Landsat imagery from 1972 to 2019 is used to generate 22 678 calving front lines across 66 Greenlandic glaciers. This improves on the state of the art in terms of the spatiotemporal coverage and accuracy of its outputs and is validated through a comprehensive intercomparison with existing studies. The current implementation offers a new opportunity to explore subseasonal and regional trends on the extent of Greenland's margins and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise.

/pdf/calving-front-machine-calfin-glacial-termini-dataset-and-5f30xb7pg3.pdf

Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019

In recent years, advances in computer hardware, graphics rendering algorithms and computer vision have enabled the utilization of 3D building reconstructions in the fields of archeological structure restoration and urban planning. This paper deals with the reconstruction of realistic 3D models of buildings facades, in the urban environment for cultural heritage. The proposed approach is an extension of our previous work in this research topic, which introduced a methodology for accurate 3D realistic facade reconstruction by defining and exploiting a relation between stereoscopic image and tacheometry data. In this work, we re-purpose well known deep neural network architectures in the fields of image segmentation and single image depth prediction, for the tasks of facade structural element detection, depth point-cloud generation and protrusion estimation, with the goal of alleviating drawbacks in our previous design, resulting in a more light-weight, robust, flexible and cost-effective design.

3D Building Façade Reconstruction Using Deep Learning

Story maps are used as an interactive tool for communication and information dissemination. A web-based application using story mapping technology is presented to explore the Methana peninsula. This volcanic area is characterized by specific volcanic geoforms, unique flora and rich history. The story map combines maps, narrative texts and multimedia content. The spatial data produce thematic maps created by a Geographic Information System on geological data, historical monuments, biodiversity and hiking paths. The purpose is to highlight the distinguishing characteristics of the Methana peninsula, to enable users to interact with maps, texts and images and to inform professional and non-professional users about the particular aspects of volcanic areas.

Creating a Story Map Using Geographic Information Systems to Explore Geomorphology and History of Methana Peninsula

We present a mobile Augmented Reality (AR) tourist guide to be utilized while walking around cultural heritage sites located in the Old town of the city of Chania, Crete, Greece. Instead of the traditional static images or text presented by mobile, location-aware tourist guides, the main focus is to seamlessly and transparently superimpose geo-located 3D reconstructions of historical buildings, in their past state, onto the real world, while users hold their consumer grade mobile phones walking on-site, without markers placed onto the buildings, offering a Mobile Augmented Reality experience. we feature three monuments; e.g., the ‘GialiTzamisi’, an Ottoman mosque; part of the south side of a Byzantine Wall and the ‘Saint Rocco’ Venetian chapel. Advances in mobile technology have brought AR to the public by utilizing the camera, GPS and inertial sensors present in modern smart phones. Technical challenges such as accurate registration of 3D and real world, in outdoors settings, have prevented AR becoming main stream. We tested commercial AR frameworks and built a mobile AR app which offers users, while visiting these monuments in the challenging outdoors environment, a virtual reconstruction displaying the monument in its past state superimposed onto the real world. Position tracking is based on the mobile phone’s GPS and inertial sensors. The users explore interest areas and unlock historical information, earning points. By combining AR technologies with locationaware, gamified and social aspects, we enhance interaction with cultural heritage sites.

Lemonia Ragia

Papers

An Architecture for Mobile Outdoors Augmented Reality for Cultural Heritage

Automatic Coastline Extraction Using Edge Detection and Optimization Procedures

3D Building Façade Reconstruction Using Deep Learning

Creating a Story Map Using Geographic Information Systems to Explore Geomorphology and History of Methana Peninsula

Outdoors Mobile Augmented Reality Application Visualizing 3D Reconstructed Historical Monuments.